Predictive Analytics In Smart Manufacturing
It involves the use of IoT devices in the assembly line to instill production intelligence in the product life cycle and improve the efficiency and productivity of manufacturing operations.
Operational technology mixes with information technology to create new opportunities for digital producers. Terabytes of data analytics produced by related products, people, and things can be accessed to extract deep insights and optimize the business and manufacturing processes to make them better than before.
In addition, the insights from such voluminous data can help manufacturers identify new revenue streams by developing high-value service offerings that focus on how products and customers can interact in the real world.
The transformation induced by smart manufacturing is rapidly changing the landscape for manufacturers, enabling them to differentiate their USPs and gain operational expertise that can disrupt production markets.
It helps to:
Make floor operation more efficient
Lower production costs
Increase the production level
Make the work environment safer
Improve visibility of the manufacturing process
Improve quality assurance levels
According to a study by BI Intelligence, the smart manufacturing market is expected to reach $ 267 billion by 2020.
The manufacturing industry forms the economic backbone of any country that is dependent on production activities for the country’s future growth.
Traditionally, process control managers manually monitor the production process and keep an eye on machine crashes. Managers must constantly be on the floor and constantly seek input from workers to monitor the process. It is not possible to predict when a piece of assembly line machine will break and stop the production process.
Industrial IoT-based smart manufacturing uses data collected from sensors placed in the assembly line to provide insight into how the production process is currently underway.
Raw data, however, are not meaningful. One cannot use it as it is to plan effective actions for the future. However, specific information extracted from data can be analyzed to generate meaningful results.
In predictive analytics, data extracted from existing datasets are used to determine usage patterns and predict possible future outcomes.
Why predictive analytics is important
For production engineers, it is important to keep the production process up and running at all times to ensure maximum production.
Predictive analysis helps manufacturing companies keep track of their assembly line machines by collecting raw data from the sensors and analyzing them to detect machine faults in the past. The software recognizes crash patterns in the past and tries to predict when a crash is likely to occur again in the future.
Using this information, the manufacturer can perform maintenance in advance to reduce the risk or prepare for the crash in advance.
By anticipating breakdowns before they occur, manufacturing companies can reduce significant losses and deal with production-related issues in a much better way. The predictive analysis, therefore, helps to perform predictive maintenance.
The ability to predict machine errors and correct them before they occur is a significant driving force behind industrial IoT investments. IoT and predictive analytics drive widespread market adoption. Manufacturers who use predictable analysis methods in their manufacturing processes report as much as 25 to 30% gains in production levels.
How does it help the manufacturer?
Production managers can know in advance which parts are likely to fail or work first. They can make effective contingency plans to deal with crashes before they occur.
A probable list of parts that may require replacement within the next few months can be used. This allows ample time to place orders in advance so that the parts can be made readily available when required.
Managers can manage inventories in a better way. They can stock up on the correct quantities of the required parts and avoid overusing less-used parts. In this way, the manufacturer can free up some capital by storing only the parts that need to be replaced frequently.
It helps keep the entire factory on track by ensuring that production equipment runs efficiently.
Managers can feel more confident that the machinery will not fail without warning while in the middle of a production run.
The manufacturer can convey this insurance to the customers to let them know that the products they have ordered will reach them on time.